Increasing the perceptual salience of relationships in parallel coordinate plots

Abstract

We present three extensions to parallel coordinates that increase the perceptual salience of relationships between axes in multivariate data sets: (1) luminance modulation maintains the ability to preattentively detect patterns in the presence of overplotting, (2) adding a one-vs.-all variable display highlights relationships between one variable and all others, and (3) adding a scatter plot within the parallel-coordinates display preattentively highlights clusters and spatial layouts without strongly interfering with the parallel-coordinates display. These techniques can be combined with one another and with existing extensions to parallel coordinates, and two of them generalize beyond cases with known-important axes. We applied these techniques to two real-world data sets (relativistic heavy-ion collision hydrodynamics and weather observations with statistical principal component analysis) as well as the popular car data set. We present relationships discovered in the data sets using these methods.

Random Per-Element Luminance Modulation for Improved Visual Tracking

“…We’ve showed that adjusting line luminance on the basis of one of the scalar variables in parallel coordinates indicates trends between that variable and others (see Figure 5). 1 This technique might also effectively indicate groupings in other visualizations. However, when nearby elements all have a similar luminance, distinguishing neighbors again becomes challenging.…”

“…1 This reveals the correlations between the first axis and each remaining axis. However, it doesn’t consistently separate local clusters of lines (see the upper halves of T_sonic and soil_H20).…”

“…In contrast, luminance-modulated parallel coordinates improve the patterns’ visual salience (see Figure 1b). 1 In particular, the line bundles shaped like an S, a U, and an inverted U in the middle of the third parameter (U_wind) are more salient. Tracing an individual line across multiple axes is also easier.…”

Exploring and visualizing multidimensional data in translational research platforms

“…Another increasingly common technique for visualizing the relationships between variables in multidimensional data sets is parallel coordinates. Here, vertical axes corresponding to each variable scaled to a common height are placed next to each other and connected with lines representing different samples [ 25 ]. This technique has been enhanced by tools such as scatter plot matrix overlay [ 26 ], proximity-based shading [ 27 ] and clustering methods that eliminate overplotting [ 28 ].…”